Interactive visualization for model interpretability
A comprehensive dashboard for understanding and debugging machine learning models through interactive visualizations of feature importance, attention patterns, and decision boundaries.
Black-box ML models make it difficult to understand why predictions are made, leading to trust issues and hidden biases. Existing tools are fragmented and lack integration.
Built a unified platform integrating SHAP, LIME, attention visualization, and concept activation vectors. Created interactive widgets for counterfactual exploration and sensitivity analysis. Implemented real-time bias detection and fairness metrics monitoring.
Adopted by 3 enterprise clients for ML governance and compliance. Helped identify and fix 12 critical model biases before production deployment.